develop automated tests

This study addresses the imbalance in the test pyramid—characterized by an overreliance on coarse-grained integration and system tests, which leads to difficulties in fault localization and slow execution—by proposing, for the first time, a method to automatically generate unit tests from existing integration tests. The approach combines static and dynamic analysis to automatically isolate component dependencies and enhance coverage at the unit level. Implemented as a Node.js tool and evaluated on twelve open-source JavaScript projects, the technique produces high-quality unit tests that significantly improve test suite structure, thereby increasing both testing efficiency and maintainability.

To address the insufficient speed, reliability, and maintainability of testing in modern software systems, this paper designs and implements a modular automated testing framework that deeply integrates Cucumber-BDD with Java. The framework introduces a novel natural-language-driven test design and engineering implementation co-development mechanism, supporting dynamic environment adaptation, reusable component-based architecture, and end-to-end automated reporting with closed-loop feedback. It integrates Selenium, TestNG, Maven, and Jenkins to enable seamless embedding into CI/CD pipelines. Empirical evaluation demonstrates a reduction of manual testing effort by over 40%, a 35% improvement in defect detection rate, and a 50% decrease in script maintenance cost. These outcomes significantly enhance agility in iterative development and streamline multi-environment one-click deployment efficiency.

Despite growing adoption of AI-driven test automation tools, their real-world efficacy—particularly in improving test efficiency, reducing maintenance costs, and enhancing defect detection—remains inadequately evaluated. Method: We conduct a systematic literature review identifying 55 tools and propose the first taxonomy of AI testing capabilities; further, we perform a dual-tool, dual-system empirical study on open-source projects, evaluating core functionalities including UI self-healing, visual testing, and intelligent test case generation. Contribution/Results: AI tools improve execution efficiency and reduce maintenance effort by over 30%, yet suffer from high false-positive rates, insufficient domain knowledge integration, and strong model dependency. This work establishes the first benchmarking framework for AI-based testing that jointly integrates a comprehensive capability taxonomy with multi-dimensional empirical validation—providing foundational guidance for developing robust, interpretable, and production-ready AI testing tools.

To address test redundancy, high feedback latency, and inconsistent pre- vs. post-commit test selection objectives in large-scale multilingual monorepos, this paper proposes the first pipeline-aware, bi-objective reinforcement learning framework for regression test optimization: failure detection is prioritized during pre-commit testing, while flaky-change identification is emphasized post-commit. The method operates entirely on language-agnostic features, integrating pipeline semantic modeling with online log analysis to support dynamically evolving industrial test suites. Evaluated on 20 weeks of real-world CI data, it achieves significantly reduced average feedback latency, a 32% improvement in pre-commit test selection precision, and a 41% reduction in false positives—without requiring expensive features such as code coverage.

To address the limitations of existing automation techniques in accuracy and efficiency for web end-to-end (E2E) testing, this paper proposes a novel feature-driven E2E test generation paradigm: leveraging large language models (LLMs) to automatically identify functional features of websites and generate semantically coherent, executable test cases. Our key contributions are threefold: (1) the first feature-driven test generation framework grounded in functional semantic reasoning; (2) E2EBench—the first benchmark explicitly designed for functional coverage evaluation; and (3) achieving 79% average feature coverage on E2EBench, outperforming the strongest baseline by 558%, thereby significantly enhancing both test completeness and semantic fidelity.

This study addresses the challenges of regression testing in remote and hybrid work environments, where communication, coordination, and quality assurance are increasingly complex. Through qualitative interviews with 20 software practitioners, complemented by process analysis, tool integration assessment, and coding of collaborative practices, the research systematically investigates the sociotechnical evolution of regression testing in distributed settings. Findings indicate that while core testing phases remain largely stable, teams increasingly rely on documentation, automation, and integrated toolchains to sustain effectiveness. Standardized reporting formats, shared repositories, and traceability mechanisms significantly mitigate collaboration barriers inherent in remote work. The study offers novel insights and practical guidance for ensuring software quality in geographically dispersed development contexts.

This work addresses the limitations of current LLM agent testing, which heavily relies on manual inspection and lacks observability into internal structures, thereby hindering automation, root cause analysis, and cost control. To bridge this gap, the paper introduces, for the first time, structured testing principles from software engineering into LLM agent evaluation, proposing a component-level automated testing approach based on execution trace tracking, LLM behavior simulation, and assertion-based validation. By integrating OpenTelemetry for fine-grained trace capture and leveraging multi-language testing frameworks with mock mechanisms, the method supports the testing automation pyramid, regression testing, and test-driven development. Experimental results demonstrate that the proposed approach significantly improves test coverage and reusability, reduces defect detection costs and testing overhead, and enables rapid root cause localization.

Traditional security testing tools deployed in CI/CD pipelines lack adaptability and struggle to effectively integrate program structure with dynamic feedback, resulting in low detection efficiency and high false-positive rates. This work presents a systematic survey of adaptive and AI-enhanced security testing approaches, introducing for the first time the notion of “structural-adaptive disconnection” to highlight the systemic misalignment between program structure representations and adaptive mechanisms. It advocates for incorporating human-in-the-loop signals into a closed-loop model refinement process. By synthesizing techniques from static and dynamic analysis, feedback-driven fuzzing, large language models, and code property graphs (CPGs), the study analyzes 55 high-quality research efforts, identifies five key open challenges, and proposes a unified research agenda for semantic-aware, feedback-driven, and multi-language-supported security testing frameworks.